Local AI models can answer the same question differently because the question is only one part of the request. The model file, system instructions, chat template, conversation history, retrieved context, runtime and sampling settings all affect what the model receives and how it generates a reply. To make results more consistent, record those inputs, use a fixed seed where available, and change one variable at a time. Repeatability is not the same as correctness: a model can give the same wrong answer on every run.
What counts as the same request?
Two runs are comparable only when the meaningful inputs and setup match. Ollama’s API accepts a model name, prompts or chat messages, system instructions, templates and generation options; its chat messages carry conversation history, and its generate endpoint also exposes a context field for continuing a conversation. See the Ollama API documentation.
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- Model artifact: Record the exact model name, version or file, and any quantization label. A different artifact is a different comparison condition.
- Prompt and instructions: Save the complete user prompt, system message and chat template.
- Context: Include all earlier turns and any retrieved documents or other context. If one run has them and another does not, the model has not received the same request.
- Runtime and settings: Record the serving software and version, sampler configuration, temperature, token filters and seed.
- Hardware and backend: Note these as part of the setup when comparing systems. The documentation cited here does not quantify how hardware or backend differences affect answers.
Model packaging is another reason to identify the exact file: llama.cpp describes GGUF as a single-file format that packages model weights, tokenizer and metadata, and its project runs models in GGUF format. That supports recording the artifact, but does not establish how much a particular quantization changes answer quality. See the llama.cpp project documentation.
Why answers vary between runs
Sampling can select different continuations
Text generation proceeds token by token. Sampling controls influence which next token is selected, so different values can change wording and, as the answer unfolds, its content. The llama.cpp server documentation lists temperature, top-k, top-p and min-p controls, as well as a seed. Its documented seed default is -1, meaning a random seed; check the documentation for the version you use because defaults can change. See the llama.cpp server documentation.
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Hidden differences change the model’s input
A changed template, system message, earlier turn or retrieved passage can alter the effective prompt even if the latest question looks identical. A changed model file or runtime can also make the setup different. Before attributing variation to randomness, compare the complete request and configuration.
Repeatability does not prove truth
A fixed seed can help reproduce output under a controlled setup. Ollama supports setting a numeric seed in its API, and llama.cpp documents a seed option. These references do not promise bit-for-bit identity across different hardware, builds, model files or runtimes. Even perfectly repeatable output may be incomplete or factually wrong; assess truth separately from consistency.
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Establish a repeatable baseline
- Choose a representative prompt. Use a task you actually care about, and save the exact wording.
- Record the full setup. Note model artifact and version, runtime and version, template, system prompt, complete conversation history, retrieved context, hardware/backend, and all generation options.
- Set a seed where supported. Use the same numeric seed and record it alongside temperature and token filters. In llama.cpp, replace the random-default seed with a fixed value for repeat trials.
- Repeat without changing anything. Send the same request under the same setup and compare the outputs. Treat the seed as a control within an implementation, not a guarantee across machines or software builds.
- Save the outputs and configuration together. This makes it possible to tell whether a later change came from a new prompt, model, runtime or setting.
Improve responses with controlled changes
Change one generation setting at a time
If the response is too variable in style, test a lower temperature or a different sampler setting while keeping the rest of the baseline fixed. Compare multiple representative prompts rather than deciding from one response. There is no universally best temperature or sampler configuration established by the cited documentation; the useful choice depends on the model and task.
Make the request more specific
State the task, intended audience, constraints and desired response format directly. Keep system instructions and templates stable while testing prompt wording, so the effect of a change is easier to see. Clearer instructions can control the request and presentation; they cannot guarantee that factual claims are accurate.
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Constrain structured output when needed
Ollama’s API supports supplying JSON as a format or a JSON schema, and its documentation recommends instructing the model in the prompt to use JSON. It states: “It’s important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.” This advice is about output formatting, not factual reliability.
Keep context stable
For a fair comparison, provide the same earlier messages and retrieved material each time. If you want to test whether added context helps, first save the baseline, then add or remove that context as the single change.
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Compare two local setups fairly
Use several representative tasks and evaluate separate outcomes rather than collapsing them into one vague judgment. A model can be consistent but wrong, or useful in substance but poor at following a requested format.
| Comparison area | What to record or assess |
|---|---|
| Model | Exact artifact or version and quantization label |
| Input | Full prompt, system instructions, template, conversation history and retrieved context |
| Runtime | Software and version, sampler sequence, temperature, token filters and seed |
| Execution setup | Hardware and backend, recorded for each run |
| Outcomes | Correctness, completeness, format compliance and run-to-run consistency, judged separately |
The cited project documentation describes configurable inputs and model packaging, not a standardized benchmark or controlled comparison of quantization and hardware effects. Avoid concluding that one quantization is always worse, or that one seed makes different environments produce identical results. Test the settings on your own task set and keep the configuration that meets your needs.
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